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Measurement in the smart factory: Improving productivity, efficiency, and safety

Dec. 9, 2019
Digitalization of instrument data enables a symphony of smarter asset management.

In this article:

In the same way that the human brain relies on sensory inputs to make sense of the world, today’s increasingly digitalized industrial systems are demanding accurate, reliable, and timely data to help inform decision-making. The upshot is that digitalization is helping to unlock the potential of instrument data to improve productivity, efficiency, and safety.

The quest to get more intelligence out of measuring instruments is not new. The introduction of the HART (Highway Addressable Remote Transducer) protocol in the mid-1980s set the foundation for overlaying a digital signal on a 4-20mA current, allowing more information than just the base process parameter to be obtained from an instrument. When declared as an open protocol, it became the technology of choice for a host of industrial applications worldwide.

Since that time, HART has been joined by a range of other digital communications protocols, including PROFIBUS, Modbus, and FOUNDATION Fieldbus. Each of these offers its own set of benefits with respect to collecting and relaying data, particularly when it comes to simplifying the approaches to resolution, diagnostics, and remote access.

The reality is that companies have been enjoying the benefits of “digital” for several decades. However, many have only been scratching the surface of the possibilities. While operators have been able to obtain a variety of data over and above the measured variable, including diagnostics, status, and alarms from multiple instruments, many have not utilized it to its fullest extent, as they lack either the software tools needed to turn data into information or the expertise and/or resources to manage and analyze it.

Digitalization is providing a growing range of opportunities for engineers and operators to access a growing range of instrument data both centrally and locally. Source: ABB

In the current environment, digitalization is changing this. Advances in key areas such as instrumentation, control systems, cloud and edge computing, and the internet of things (IoT) devices have exponentially increased the breadth of deployment and overall availability of processing capabilities. This has increased the amount of data that can be realistically extracted and subsequently transmitted and/or stored from instruments and other devices. When subjected to advanced algorithms and tools, this information-rich data provides the basis for informed decision-making and predictive or forensic analysis of the process or supporting infrastructure.

Digging deeper with digitalization

With digitalization has come the opportunity to dig deeper into data, extracting added value by enabling the digital functions in both legacy and new devices to be utilized to their fullest extent. This offers several strategic benefits that open new possibilities for greater plant efficiency at both operational and maintenance levels.

One example is the ability to obtain additional useful data from an instrument beyond its normal primary function that can be used to assess process performance. Chemical analyzers and level transmitters, for example, can also provide parameters such as temperature that can be used to expand the total number of measurement points at a site. These can be included in advanced data analytics approaches that yield more robust information to make better process control decisions. Similarly, data from electromagnetic flowmeters that measure conductivity in a pipe can help flag changes over time that might indicate the need to adjust chemical dosing levels in water treatment processes, for example. 

With the arrival of modular software solutions, it is possible to share these data insights more easily across an organization, avoiding the risk of overlooking potentially useful information that could otherwise be lost in the mountains of data being generated. The increased data available can be quickly processed, filtered, and initially interpreted through data analytics tools, allowing information to be presented to users in context. 

This improved sharing of data delivers benefits on multiple levels. At the enterprise level, it can help optimize asset management and resource planning by allowing decisions to be based on the latest process data. It can also help facilitate improved workforce management by equipping plant-floor staff or engineers with the information needed to respond to situations such as system alarms, warnings or other issues.

Vitally, it can also help to deliver the information needed for maintaining long-term asset health by providing continuous monitoring and assessment of instrument performance that can then be used as the basis for predictive and preventive maintenance planning. Knowing in advance when your instrument or process is going to need attention can help save a lot of time and trouble down the line, as by mitigating the potential risk of noncompliance with regulations. The ability to access deeper and more detailed diagnostic and operational data can help team members pinpoint potential problems before they have the chance to escalate.

Use of this interpreted data further facilitates the transition away from maintenance being conducted according to a strict schedule and toward maintenance performed based on the device condition. In addition to ensuring smooth process performance and eliminating unplanned downtime, this can be beneficial when it comes to resource deployment, with engineers only having to be deployed to sites when necessary and maintenance parts being replaced only when needed. 

These benefits also apply to the process being measured. By using and analyzing the richer data sets from instruments throughout the process, operators can spot or be warned about potential upsets or unexpected variations in performance, enabling them to take corrective action in good time. This same data analytics approach can be used to find hidden optimization opportunities through correlation of seemingly unrelated measurements and other process information.

Look to the cloud

The introduction of cloud-based infrastructures within industrial organizations is transforming the possibilities for deriving maximum value from data. Via the cloud, people and systems alike can share and directly or remotely access inputs from process-line instrumentation and signals directed to a variety of control equipment such as programmable logic control (PLC), supervisory control and data acquisition (SCADA), and distributed control (DCS) systems.

Advanced data analysis along with developments in display technology means that all of this data can be viewed in a variety of situationally dependent formats. Users can view and interact with data on large touchscreen displays in control rooms, choosing between different views of their process and calling up everything from reports to real-time and/or historic process data. While these capabilities aren’t new, the simplicity and flexibility of adapting the presentation of information to suit different users has increased dramatically.

Apps running on mobile devices such as smartphones and tablets also enable personnel to interact with instruments and other process equipment. Technologies such as Bluetooth and NFC (near-field communication) make it possible for smart portable devices to be used to poll, troubleshoot, and configure instruments in the field, making it faster and less costly to do so. The inclusion of web servers in these same instruments allows users to remotely scroll through menus and instrument health information and to make or change settings and navigate among different data views.

With many organizations facing a shortage of experienced operating staff, there is also a move toward leveraging technologies from the consumer world that can help simplify tasks by providing a familiar and easy-to-learn interface. One example is the use of QR codes to indicate and report problems. Just by scanning the code with a mobile phone app, an operator can relay data about an instrument to the instrument’s manufacturer, which can provide expert-based remote assistance.

Digitalization is what you make it

A key thing to remember about digitalization is that there is no single, one-size-fits-all answer that can be installed and left to run by itself. What you end up doing very much depends on where you are now and what you are looking to achieve.

About the Author: Dane Maisel

Dane Maisel is vice president of business development for ABB Measurement & Analytics Americas.

Depending on your expectations, experience, and willingness to invest, digitalization can be a series of incremental changes within a department/site or a sweeping transformation across multiple locations of an enterprise.

So how should you go about the digital transition at your company? Deriving maximum benefit from a digital installation requires careful planning and setting of expectations and goals.

It is important to remember, though, that digitalization itself is a tool, not a goal. Start by identifying which part of your process has a problem you want to solve or an area that you want to optimize. Let that drive and prioritize your digitalization approach by helping you determine which aspects of the process could provide the information needed to achieve your goal. This will likely involve a combination of using existing instrumentation and data and investing in additional smart devices to provide further data that may be needed. In this way, driving from your current process to fulfilling your needs helps avoid the pitfall of seeing digital as an end rather than a means to an end.

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